From To-Do to Ta-Da: Transforming Task-Focused IR with Generative AI

For decades, scholars have emphasized that tasks should be the central focus in Information Retrieval (IR).This point of view holds even more significance with the advent of Generative Artificial Intelligence (GenAI) models, which can, among other capabilities, understand natural language, engage in dialog with users, generate bespoke user interfaces, and power agents to help complete tasks.GenAI presents an unprecedented opportunity to finally realize the potential of tasks in IR, enhance task-focused retrieval and interaction, and create "magical" task completion moments for users.In this paper, we explore the rationale and methodology behind this argument.Traditional IR systems support mostly simple tasks.The emergence of GenAI creates an opportunity for IR systems to help users achieve complex tasks and for the IR community to rekindle its interest and demonstrate leadership in this sizable and significant problem space.We underscore the pivotal role of tasks in IR and introduce new evidence supporting the notion that task-centric approaches, abstracted from specific modalities, represent the future of IR.Building on this foundation, we envision the development, utilization, and evaluation of next-generation IR systems.We propose a promising future where IR agents prioritize users, their tasks, and their situations.However, despite their potential to address task-focused and modality-independent IR, agents alone are insufficient.We propose a robust ecosystem around these agents that transcends traditional queries, questions, prompts, and modalities to address users' fundamental needs, tasks, and goals.